Senior Generative AI Engineer

Job Summary :

We are seeking a skilled Senior Generative AI Engineer to design, develop, and deploy enterprise-grade AI solutions powered by Large Language Models (LLMs), Agentic AI frameworks, and cloud-native architectures. The ideal candidate will have strong hands-on software engineering experience with Python, proven experience building GenAI products from concept to production, and a deep understanding of RAG, multi-agent systems, agent orchestration frameworks, observability, and AI application deployment.

Key Responsibilities :

- Design, develop, and deploy enterprise-grade GenAI, RAG, and Agentic AI solutions.

- Build multimodal AI agents and multi-agent workflows using LangChain and LangGraph frameworks.

- Develop scalable backend services and APIs using Python; build user interfaces using Streamlit and React.

- Develop and deploy solutions on Databricks using Repos, Jobs, MLflow, Model Serving, and Unity Catalog.

- Implement observability, tracing, logging, monitoring, evaluation, and optimization frameworks for GenAI applications.

- Collaborate with stakeholders to translate business requirements into production-ready AI solutions.

Required Skills :

- 4 - 8 years of experience in Python development, AI/ML, and Generative AI.

- Strong hands-on experience delivering GenAI/Agentic AI solutions in production environments.

- Hands-on experience with LangChain, LangGraph, AI Agents, and workflow orchestration.

- Designing workflows involving planning, tool calling, memory, and autonomous task execution.

- Strong understanding of : RAG, Embeddings, Vector Search, Vector databases specifically (FAISS, Chroma, Pinecone, Azure AI Search, etc.), Prompt Engineering, LLM Optimization, and Semantic Search.

- Practical experience with Microsoft Copilot, ChatGPT, Azure OpenAI/OpenAI, and open-source LLMs.

- Strong Python programming, SQL, API development, and debugging skills. Following software engineering practices such as code reviews, testing, documentation, and version control.

- Experience with Databricks, MLflow, Model Serving, Unity Catalog, and cloud-based AI workloads is preferred.

- Experience implementing observability, tracing, monitoring, and troubleshooting for AI applications.

- Experience with CI/CD, MLOps, AI governance, and production deployment best practices.

- Monitoring hallucinations, retrieval quality, latency, and token consumption. Building evaluation and benchmarking frameworks.

Education :

- Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.